Forest Cover Type Prediction Based Linear Regression
Year: 2024
Type: article
Abstract: Nowadays, the relentless exploitation of natural resources and the escalating deforestation have cast a dark shadow over the survival of forests worldwide.With the ever-growing demand for raw materials and the quest for economic growth, many industries have turned their sights on forests as a ready source of timber, minerals, and other valuable resources. Forests, repositories of biodiversity and crucial to our planet's ecological balance, are perilously close to disappearing, calling for urgent measures to ensure their sustainability. This study delves into the application of machine learning in predicting forest cover types, focusing on various methodologies, particularly linear regression. In this paper, the analysis mainly offers a thought-provoking assessment of the pros and cons of linear regression, unpacking its intricate strengths and weaknesses with clarity and wit. By examining this paper, individuals can gain a comprehensive understanding of how machine learning can aid in the planning and management of forest resources.
DOI: 10.54097/8btfp593
Author: Xiaoyu Gao
Institution: Nanjing Foreign Language School
Language: English
Subfield: Media Technology
Field: Engineering
Domain: Physical Sciences
SDG: Life on land
Open Access status: diamond
OpenAlex ID: W4401667176